Biodiesel content determination in diesel fuel blends using near infrared (NIR) spectroscopy and support vector machines (SVM)

Alves, Julio Cesar Laurentino; Poppi, Ronei J.

doi:10.1016/j.talanta.2012.11.033

Cited by 89 publications

(36 citation statements)

References 45 publications

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“…34,35 The root mean squared error of cross-validation (RMSECV), root mean squared error of prediction (RMSEP) and correlation coefficient (R) were employed as indexes for the performance of the achieved model. 34,35 The root mean squared error of cross-validation (RMSECV), root mean squared error of prediction (RMSEP) and correlation coefficient (R) were employed as indexes for the performance of the achieved model.…”

Section: View Article Onlinementioning

confidence: 99%

Rapid and nondestructive evaluation of fish freshness by near infrared reflectance spectroscopy combined with chemometrics analysis

et al. 2014

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Rapid and nondestructive measurement of freshness is essential for the control of fish and the quality and safety of its products. In this study, K value was measured by high performance liquid chromatography (HPLC) and employed as an index of fish freshness. The prediction models of the silver chub freshness were developed using Fourier Transform Near Infrared Reflectance Spectroscopy (FT-NIRS) with Several Partial Least Squares (PLS, i-PLS, Si-PLS), Support Vector Machines Regression (SVMR) and Synergy interval plus Support Vector Machine Regression leading to Si-SVMR. By comparison, the performance of Si-SVMR model was superior to the others for the prediction of K value, where RMSECV ¼ 0.027095 and R c ¼ 95.59% for the calibration set, whereas RMSEP ¼ 0.036525 and R p ¼ 93.74% for the prediction set.The results indicated that FT-NIR spectroscopy together with Si-SVMR model could be a reliable method for the detection of fish freshness.

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confidence: 99%

Rapid and nondestructive evaluation of fish freshness by near infrared reflectance spectroscopy combined with chemometrics analysis

et al. 2014

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“…Biodiesel is a renewable fuel [1 -5] considered the main substitute of fossil fuels [1,6,7]. It presents reduced exhaust emissions in comparison to petroleum diesel [8 -10], it is biodegradable [2,4,9,11,12], and it is miscible in all proportions to petroleum diesel [9].…”

Section: Introductionmentioning

confidence: 99%

Biodiesel Synthesis Monitoring using Near Infrared Spectroscopy

Gelinski¹,

Hamerski²,

Corazza³

et al. 2018

TOCENGJ

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Objective: Biodiesel is a renewable fuel considered as the main substitute for fossil fuels. Its industrial production is mainly made by the transesterification reaction. In most processes, information on the production of biodiesel is essentially done by off-line measurements. Methods: However, for the purpose of control, where online monitoring of biodiesel conversion is required, this is not a satisfactory approach. An alternative technique to the online quantification of conversion is the near infrared (NIR) spectroscopy, which is fast and accurate. In this work, models for biodiesel reactions monitoring using NIR spectroscopy were developed based on the ester content during alkali-catalyzed transesterification reaction between soybean oil and ethanol. Gas chromatography with flame ionization detection was employed as the reference method for quantification. FT-NIR spectra were acquired with a transflectance probe. The models were developed using Partial Least Squares (PLS) regression with synthetic samples at room temperature simulating reaction composition for different ethanol to oil molar ratios and conversions. Model predictions were then validated online for reactions performed with ethanol to oil molar ratios of 6 and 9 at 55ºC. Standard errors of prediction of external data were equal to 3.12%, hence close to the experimental error of the reference technique (2.78%), showing that even without using data from a monitored reaction to perform calibration, proper on-line predictions were provided during transesterification runs. Results: Additionally, it is shown that PLS models and NIR spectra of few samples can be combined to accurately predict the glycerol contents of the medium, making the NIR spectroscopy a powerful tool for biodiesel production monitoring.

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“…37 There is also a combination band of C-H bond stretching and C]O bond stretching near 4650 cm À1 . 37 Moreover the difference in NIR spectra in the region of 4425 cm À1 and 6005 cm À1 , probably related to stretching of the terminal methyl group near the carbonyl group, where methyl esters have peaks while triglycerides exhibit only shoulders, provides the biodiesel quantication in a selective manner, as demonstrated in previous studies, 22,38 without interference from the possible presence of vegetable oil in the mixture. Fig.…”

Section: Data Treatment and Data Analyses Methodsmentioning

confidence: 53%

Simultaneous determination of hydrocarbon renewable diesel, biodiesel and petroleum diesel contents in diesel fuel blends using near infrared (NIR) spectroscopy and chemometrics

Alves

Poppi

2013

Analyst

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Highly polluting fuels based on non-renewable resources such as fossil fuels need to be replaced with potentially less polluting renewable fuels derived from vegetable or animal biomass, these so-called biofuels, are a reality nowadays and many countries have started the challenge of increasing the use of different types of biofuels, such as ethanol and biodiesel (fatty acid alkyl esters), often mixed with petroleum derivatives, such as gasoline and diesel, respectively. The quantitative determination of these fuel blends using simple, fast and low cost methods based on near infrared (NIR) spectroscopy combined with chemometric methods has been reported. However, advanced biofuels based on a mixture of hydrocarbons or a single hydrocarbon molecule, such as farnesane (2,6,10-trimethyldodecane), a hydrocarbon renewable diesel, can also be used in mixtures with biodiesel and petroleum diesel fuel and the use of NIR spectroscopy for the quantitative determination of a ternary fuel blend of these two hydrocarbon-based fuels and biodiesel can be a useful tool for quality control. This work presents a development of an analytical method for the quantitative determination of hydrocarbon renewable diesel (farnesane), biodiesel and petroleum diesel fuel blends using NIR spectroscopy combined with chemometric methods, such as partial least squares (PLS) and support vector machines (SVM). This development leads to a more accurate, simpler, faster and cheaper method when compared to the standard reference method ASTM D6866 and with the main advantage of providing the individual quantification of two different biofuels in a mixture with petroleum diesel fuel. Using the developed PLS model the three fuel blend components were determined simultaneously with values of root mean square error of prediction (RMSEP) of 0.25%, 0.19% and 0.38% for hydrocarbon renewable diesel, biodiesel and petroleum diesel, respectively, the values obtained were in agreement with those suggested by reference methods for the determination of renewable fuels.

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Biodiesel content determination in diesel fuel blends using near infrared (NIR) spectroscopy and support vector machines (SVM)

Cited by 89 publications

References 45 publications

Rapid and nondestructive evaluation of fish freshness by near infrared reflectance spectroscopy combined with chemometrics analysis

Rapid and nondestructive evaluation of fish freshness by near infrared reflectance spectroscopy combined with chemometrics analysis

Biodiesel Synthesis Monitoring using Near Infrared Spectroscopy

Simultaneous determination of hydrocarbon renewable diesel, biodiesel and petroleum diesel contents in diesel fuel blends using near infrared (NIR) spectroscopy and chemometrics

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